System and method for generating battery alarms in infusion devices

ABSTRACT

A system and method is disclosed for detecting remaining battery voltage or capacity in an infusion device and generating alarms based on the detection. The battery lifetime extension method includes providing an infusion device that derives its power from a rechargeable battery. The infusion device may derive its power from a rechargeable battery. Furthermore, the infusion device receives, at predetermined intervals of time in real-time sensor data comprising: a voltage, a change in the voltage over the predetermined interval of time, an average current, a temperature, and a remaining voltage or capacity reported by a battery gas gauge integrated circuit (“IC”) associated with the rechargeable battery. An improved and customized neural network model utilizes the sensor data to determine an indicia of the actual remaining voltage or capacity of the rechargeable battery in real-time. The indicia may be used to lengthen and/or abate ongoing medical infusion therapy.

PRIORITY CLAIM

This application claims priority to and the benefit as a non-provisionalapplication of U.S. Provisional Patent Application No. 63/132,177, filedDec. 30, 2020, the entire contents of which are hereby incorporated byreference and relied upon.

BACKGROUND

Generally, medical patients sometimes require precise intravenous (“IV”)delivery of either continuous medication or medication at set periodicintervals using infusion pumps. Known infusion pumps provide controlledfluid medication or drug infusion where the fluid can be administered ata precise rate that keeps a medication/drug concentration within atherapeutic margin and out of an unnecessary or possibly toxic range.The infusion pumps provide appropriate medication/drug delivery to apatient at a controllable rate, which does not require frequentattention.

Infusion pumps may facilitate administration of intravenous therapy topatients both in and outside of a clinical setting. Outside a clinicalsetting, doctors have found that in many instances patients can returnto substantially normal lives, provided that they receive periodic orcontinuous intravenous administration of medication, drugs, or otherfluids such as saline. Among the types of therapies requiring this kindof administration are antibiotic therapy, chemotherapy, pain controltherapy, nutritional therapy, and several other types that are known bythose skilled in the art. In many cases, patients receive multiple dailytherapies. Certain medical conditions require infusion of drugs in asolution over relatively short periods such as from thirty minutes totwo hours. These conditions and others have collectively promoted thedevelopment of increasingly lightweight, portable or ambulatory infusionpumps that can be worn by a patient and are capable of administering acontinuous supply of medication at a desired rate, or providing severaldoses of medication at scheduled intervals.

Known infusion pumps include elastomeric pumps, which squeeze solutionfrom flexible containers, such as balloons, into IV tubing for deliveryto a patient. Alternatively, infusion pumps may include spring-loadedpumps that pressurize solution containers or reservoirs. Certain pumpdesigns utilize cartridges containing flexible compartments that aresqueezed by pressure rollers for discharging the solutions. Further,known infusion pumps include peristaltic pumps having finger actuatorsor a roller actuator that apply pressure to IV tubing for deliveringfluid from a fluid container to a patient.

Infusion pumps utilizing syringes are also known. These syringe pumpsuse a drive mechanism to move a plunger of a syringe to deliver a fluidto a patient. Typically, these infusion pumps include a housing adaptedto receive a syringe assembly, a drive mechanism adapted to move thesyringe plunger, and a pump control unit having a variety of operatingcontrols.

Most known infusion systems use a rechargeable battery to provide powerwhen the system is not plugged into AC power. These rechargeablebatteries typically include “smart” batteries, as they include a batterygas gauge integrated circuit (“IC”). The battery gas gauge IC providesdata regarding a current status of the battery, including its remainingvoltage or capacity. The battery gas gauge IC can also measure a batterycell voltage, a temperature, and a current to determine the remainingvoltage or capacity of the battery, e.g., by determining a total chargegoing into and coming out of the battery, and by determining an internalimpedance of the battery. The calculated impedance of the battery can becompared to battery impedance profiles stored in the battery gas gaugeIC to estimate the remaining voltage or capacity of the battery.However, this estimated remaining voltage or capacity can often beerroneous, due to factors that are not known to the battery gas gaugeIC.

Furthermore, the power management software of the infusion system mayuse this erroneous remaining voltage or capacity, reported by thebattery gas gauge IC, to calculate the remaining runtime of the infusionsystem (“run-time remaining”), which is the amount of time the infusionsystem can continue to deliver medication to the patient until thebattery is fully depleted. The run-time remaining value can be used todetermine when “low”, “very low”, and “depleted” battery alarms shouldbe issued. However, since the remaining voltage or capacity valuereported by the battery gas gauge IC can be inherently inaccurate, therun-time remaining value may also be inaccurate. This leads tosituations where battery alarms indicating low levels of battery voltageor capacity are issued at incorrect times, leading to situations wherethe infusion pump cannot run for as long as needed after battery alarmsare issued. This can lead to undesirable clinical outcomes, such as anunexpected interruption of an infusion therapy.

To compensate for these shortcomings, a margin of time is often added tothe calculated run-time remaining value in order to ensure that theinfusion system can run for a desired length of time. However, in moretypical situations, this added margin can often result in shutting downthe system even when there may still be remaining voltage or capacityleft in the battery.

Accordingly, a more reliable and accurate method and system fordetecting and issuing alarms based on remaining battery voltage orcapacity is desired.

SUMMARY

The present disclosure provides a new and innovative method and systemfor detecting remaining battery voltage or capacity and generatingalarms based on the detection. In various embodiments, the deviceutilizing the disclosed method and system for the detection and thealarming of remaining battery voltage or capacity is an infusion pump.The infusion pump may comprise a peristaltic pump, a syringe pump, or anambulatory pump configured to deliver a medication to a patient. Itshould be appreciated that the device is in various embodiments, anytype of medical device, or any other suitable device having arechargeable battery.

The disclosed method includes using software run by an infusion deviceto monitor the remaining battery voltage or capacity and generate alarmsif the remaining battery voltage or capacity falls below predeterminedthresholds (e.g., if the remaining battery voltage or capacity indicates“low battery,” “very low battery,” or a “depleted battery”). Thesoftware may comprise instructions stored in a memory of the infusiondevice, and may be executable by one or more processors of the infusiondevice. Furthermore, the infusion device may derive its power from arechargeable battery, and may receive various data from the rechargeablebattery e.g., via sensors. In one embodiment, the infusion device mayreceive in real-time, at predetermined intervals of time, measurementsincluding a voltage of the rechargeable battery, a change in the voltageover the predetermined interval of time, an average current associatedwith the rechargeable battery, a temperature of the rechargeablebattery, and/or a remaining voltage or capacity reported by a batterygas gauge integrated circuit (“IC”) associated with the rechargeablebattery. The received measurements may be used to generate a featurevector.

The method may further comprise deploying the feature vector into aneural network previously trained to determine an actual remainingvoltage or capacity of the rechargeable battery. The trained neuralnetwork may comprise weight factors and biases calculated for aplurality of paths through a plurality of layers (e.g., an input layer,a plurality of hidden layers, and an output layer). Furthermore, theneural network may be trained from a training dataset comprising theabove measurements from reference data (e.g., the above measurementsfrom other rechargeable batteries) with known and actual remainingcapacities. After deployment of the feature vector into the trainedneural network, the infusion device is configured to determine anindicia of the actual remaining voltage or capacity of the rechargeablebattery in real-time based on the measurements it received at a giveninterval of time. In some aspects, the indicia of the actual remainingvoltage or capacity may indicate whether the actual remaining voltage orcapacity satisfies a predetermined threshold for a low battery voltageor capacity, a very low battery voltage or capacity, and/or a depletedbattery voltage or capacity. The infusion device may generate an alarmif one or more of these thresholds are met.

It has been shown that determining remaining battery voltage or capacitythrough the artificial neural network, and generating alarms basedaccordingly, is significantly more accurate and reliable thanconventional methods. Thus, the systems and methods disclosed hereinreduce the time and effort spent towards mitigating the effect ofinaccurate or erroneous indications of remaining battery voltage orcapacity found using conventional methods. An additional benefit of thedisclosed method includes an improvement to medical care of the patient,as there will be less interruptions in infusion therapy as a result ofunreliable indications of battery depletion.

In light of the disclosure herein and without limiting the disclosure inany way, in a first aspect of the present disclosure, which may becombined with any other aspect listed herein unless specified otherwise,an infusion device includes a rechargeable battery having a gas gaugeintegrated circuit (“IC”), one or more processors, and memory storinginstructions that, when executed by the one or more processors, causethe one or more processors to receive, at predetermined intervals oftime in real-time, measurements comprising a voltage of the rechargeablebattery, a change in the voltage over the predetermined interval oftime, an average current associated with the rechargeable battery, atemperature of the rechargeable battery, and a remaining voltage orcapacity reported by the gas gauge IC. The one or more processors arealso configured to generate a feature vector comprising the voltage, thechange in the voltage, the average current, the temperature, theremaining voltage or capacity reported by the gas gauge IC, and a fullcharge voltage or capacity of the rechargeable battery and apply thefeature vector to a trained neural network to determine an actualremaining voltage or capacity of the rechargeable battery. The trainedneural network comprises weight factors and biases for calculating aplurality of paths through a plurality of layers. The one or moreprocessors are further configured to generate, in real-time, an alarmindicating that the actual remaining voltage or capacity of therechargeable battery is below a predetermined threshold when the actualremaining voltage or capacity of the rechargeable battery is below thepredetermined threshold.

In accordance with a second aspect of the present disclosure, which maybe used in combination with any other aspect listed herein unless statedotherwise, the predetermined threshold includes a first thresholdcorresponding to a low battery state, a second threshold correspondingto a very low battery state, and a third threshold corresponding to adepleted battery state.

In accordance with a third aspect of the present disclosure, which maybe used in combination with any other aspect listed herein unless statedotherwise, the trained neural network is configured to use the featurevector to determine if any of the first, second, or third thresholds aresatisfied, when the first threshold is reached and the second thresholdis not reached, indicate the low battery state for the alarm, when thefirst and second thresholds are reached and the third threshold is notreached, indicate the very low battery state for the alarm, and when thefirst, second, and third thresholds are reached, indicate the depletedbattery state for the alarm.

In accordance with a fourth aspect of the present disclosure, which maybe used in combination with any other aspect listed herein unless statedotherwise, the low battery state corresponds to 30 minutes before thedepleted battery state is reached and the very low battery statecorresponds to 15 minutes before the depleted battery state is reached.

In accordance with a fifth aspect of the present disclosure, which maybe used in combination with any other aspect listed herein unless statedotherwise, the depleted battery state corresponds to three to fourminutes before the rechargeable battery is depleted and can no longerprovide power.

In accordance with a sixth aspect of the present disclosure, which maybe used in combination with any other aspect listed herein unless statedotherwise, the one or more processors are configured to generate featurevectors and apply the feature vectors in real-time to the trained neuralnetwork at periodic intervals including at least one of every 50milliseconds, 100 milliseconds, 500 milliseconds, 1 second, 2 seconds, 5seconds, 30 seconds, or 1 minute.

In accordance with a seventh aspect of the present disclosure, which maybe used in combination with any other aspect listed herein unless statedotherwise, the one or more processors are configured to transmit thealarm to a server via a network.

In accordance with an eighth aspect of the present disclosure, which maybe used in combination with any other aspect listed herein unless statedotherwise, the one or more processors are configured to display anindication of the alarm on a user interface.

In accordance with a ninth aspect of the present disclosure, which maybe used in combination with any other aspect listed herein unless statedotherwise, an infusion device includes a rechargeable battery having agas gauge integrated circuit (“IC”), a user interface, a battery sensor,one or more processors, and memory storing a plurality of trained neuralnetworks for different rechargeable battery types and instructions that,when executed by the one or more processors, cause the one or moreprocessors to receive from the gas gauge IC information indicative of atype of the rechargeable and select one of the trained neural networksbased on the information from the gas gauge IC. The one or moreprocessors are also configured to at least one of receive or determine,at predetermined intervals of time in real-time, measurements comprisinga voltage of the rechargeable battery from the battery sensor, a changein the voltage over the predetermined interval of time, an averagecurrent associated with the rechargeable battery from the batterysensor, a temperature of the rechargeable battery from the batterysensor, and a remaining voltage or capacity reported by the gas gaugeIC. The one or more processors are further configured to generate afeature vector comprising the voltage, the change in the voltage, theaverage current, the temperature, the remaining voltage or capacityreported by the gas gauge IC, and a full charge voltage or capacity ofthe rechargeable battery and apply the feature vector to the selectedtrained neural network to determine an actual remaining voltage orcapacity of the rechargeable battery. The trained neural networkcomprises weight factors and biases for calculating a plurality of pathsthrough a plurality of layers. The one or more processors areadditionally configured to generate, in real-time, an alarm indicatingthat the actual remaining voltage or capacity of the rechargeablebattery is below a predetermined threshold when the actual remainingvoltage or capacity of the rechargeable battery is below thepredetermined threshold.

In accordance with a tenth aspect of the present disclosure, which maybe used in combination with any other aspect listed herein unless statedotherwise, the predetermined threshold includes a first thresholdcorresponding to a low battery state, a second threshold correspondingto a very low battery state, and a third threshold corresponding to adepleted battery state.

In accordance with an eleventh aspect of the present disclosure, whichmay be used in combination with any other aspect listed herein unlessstated otherwise, the trained neural network is configured to use thefeature vector to determine if any of the first, second, or thirdthresholds are satisfied, when the first threshold is reached and thesecond threshold is not reached, indicate the low battery state for thealarm, when the first and second thresholds are reached and the thirdthreshold is not reached, indicate the very low battery state for thealarm, and when the first, second, and third thresholds are reached,indicate the depleted battery state for the alarm.

In accordance with a twelfth aspect of the present disclosure, which maybe used in combination with any other aspect listed herein unless statedotherwise, the one or more processors are configured to transmit thealarm to a server via a network.

In accordance with a thirteenth aspect of the present disclosure, whichmay be used in combination with any other aspect listed herein unlessstated otherwise, the one or more processors are configured to displayan indication of the alarm on the user interface.

In accordance with a fourteenth aspect of the present disclosure, whichmay be used in combination with any other aspect listed herein unlessstated otherwise, an infusion system includes a server configured togenerate a plurality of trained neural networks and an infusion devicecommunicatively coupled to the server via a network. The infusion deviceincludes a rechargeable battery having a gas gauge integrated circuit(“IC”), one or more processors, and memory storing instructions that,when executed by the one or more processors, cause the one or moreprocessors to receive at least one trained neural network from theserver. The received trained neural network comprises weight factors andbiases for calculating a plurality of paths through a plurality oflayers. The one or more processors are also configured to receive, atpredetermined intervals of time in real-time, measurements comprising avoltage of the rechargeable battery, a change in the voltage over thepredetermined interval of time, an average current associated with therechargeable battery, a temperature of the rechargeable battery, and aremaining voltage or capacity reported by the gas gauge IC. The one ormore processors are further configured to generate a feature vectorcomprising the voltage, the change in the voltage, the average current,the temperature, the remaining voltage or capacity reported by the gasgauge IC, and a full charge voltage or capacity of the rechargeablebattery, apply the feature vector to the received trained neural networkto determine an actual remaining voltage or capacity of the rechargeablebattery, and generate, in real-time, an alarm indicating that the actualremaining voltage or capacity of the rechargeable battery is below apredetermined threshold when the actual remaining voltage or capacity ofthe rechargeable battery is below the predetermined threshold.

In accordance with a fifteenth aspect of the present disclosure, whichmay be used in combination with any other aspect listed herein unlessstated otherwise, the server is configured to generate the plurality oftrained neural networks for different rechargeable battery types,receive an indication of a rechargeable battery type of the infusiondevice, select a trained neural network that corresponds to therechargeable battery type at the infusion device, and transmit theselected trained neural network to the infusion device.

In accordance with a sixteenth aspect of the present disclosure, whichmay be used in combination with any other aspect listed herein unlessstated otherwise, the server is configured to, for each rechargeablebattery type, generate, for each of a plurality of reference dataobtained during discharging of reference batteries, a reference featurevector comprising a reference voltage of a reference battery, a changein the reference voltage over a predetermined interval of time, areference average current associated with the reference battery, areference temperature associated with the reference battery, and areference remaining voltage or capacity reported by a battery gas gaugeintegrated circuit (“IC”) associated with the reference battery. Theserver is also configured to, for each rechargeable battery type,associate, for each of the plurality of reference data, the referencefeature vector with a corresponding output vector indicating an actualreference remaining voltage or capacity and train, using the associatedreference feature vectors, one of the neural networks to determine theactual remaining voltage or capacity of the rechargeable battery type.

In accordance with a seventeenth aspect of the present disclosure, whichmay be used in combination with any other aspect listed herein unlessstated otherwise, the predetermined threshold includes a first thresholdcorresponding to a low battery state, a second threshold correspondingto a very low battery state, and a third threshold corresponding to adepleted battery state.

In accordance with an eighteenth aspect of the present disclosure, whichmay be used in combination with any other aspect listed herein unlessstated otherwise, the received trained neural network is configured touse the feature vector to determine if any of the first, second, orthird thresholds are satisfied, when the first threshold is reached andthe second threshold is not reached, indicate the low battery state forthe alarm, when the first and second thresholds are reached and thethird threshold is not reached, indicate the very low battery state forthe alarm, and when the first, second, and third thresholds are reached,indicate the depleted battery state for the alarm.

In accordance with a nineteenth aspect of the present disclosure, whichmay be used in combination with any other aspect listed herein unlessstated otherwise, the one or more processors are configured to transmitthe alarm to a server via the network.

In accordance with a twentieth aspect of the present disclosure, whichmay be used in combination with any other aspect listed herein unlessstated otherwise, the one or more processors are configured to displayan indication of the alarm on the user interface.

In accordance with a twenty-first aspect of the present disclosure, anyof the structure, functionality, and alternatives disclosed inconnection with any one or more of FIGS. 1 to 7 may be combined with anyother structure, functionality, and alternatives disclosed in connectionwith any other one or more of FIGS. 1 to 7.

In light of the present disclosure and the above aspects, it istherefore an advantage of the present disclosure to provide an infusionsystem configured to use a trained neural network to determine when abattery reaches a low state, a very low state, and a depletion state.

It is another advantage of the present disclosure to use a trainedneural network to overcome inaccuracies of using a battery's internalgas gauge to measure remaining battery voltage or capacity.

Additional features and advantages are described in, and will beapparent from, the following Detailed Description and the Figures. Thefeatures and advantages described herein are not all-inclusive and, inparticular, many additional features and advantages will be apparent toone of ordinary skill in the art in view of the figures and description.Also, any particular embodiment does not have to have all of theadvantages listed herein and it is expressly contemplated to claimindividual advantageous embodiments separately. Moreover, it should benoted that the language used in the specification has been selectedprincipally for readability and instructional purposes, and not to limitthe scope of the inventive subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart illustrating an example artificial neuralnetwork, according to an embodiment of the present disclosure.

FIG. 2 is a diagram illustrating an input data matrix for using anartificial neural network for detecting, and generating alarms based on,remaining battery voltage, according to an embodiment of the presentdisclosure.

FIG. 3 is a diagram showing corresponding target vectors for the inputdata matrix of FIG. 2, according to an embodiment of the presentdisclosure.

FIG. 4 is a flow chart illustrating an example method for training aneural network for detecting, and generating alarms for, remainingbattery voltage, according an embodiment of the present disclosure.

FIG. 5 is a flow chart illustrating an example method for applying atrained neural network for detecting, and generating alarms for,remaining battery voltage, according an embodiment of the presentdisclosure.

FIG. 6 is a diagram of an infusion device that is configured to use atrained neural network to perform the method of FIG. 5, according to anexample embodiment of the present disclosure.

FIG. 7 is a diagram of an infusion system configured to perform theoperations described in connection with FIG. 4, according to an exampleembodiment of the present disclosure.

DETAILED DESCRIPTION

Monitoring remaining battery voltage and generating alarms for lowremaining battery voltage levels or capacity for providing a charge arecritical safety measures for medication delivery infusion systems. Lowremaining battery voltage levels may include “low,” “very low,” and“depleted” remaining battery voltage levels, each of which may triggeran alarm if detected. For each alarm, there may be a specified remainingtime for infusion by United States Food and Drug Administration (“FDA”)regulations and manufacturer requirements. However, since batteries mayvary as a result of different initial conditions and dischargingcharacteristics, false alarms are often observed, e.g., due to falsemeasurements of remaining battery capacities.

Disclosed herein are novel and nonobvious systems and methods forbattery alarms and remaining battery voltage detection using neuralnetwork models, which significantly improve known battery alarm andremaining battery voltage detection systems, and make infusion devicessafer for patients. The disclosed method combines the benefits of a moreaccurate and reliable reporting of battery voltage while reducing oreliminating time and labor typically spent to calibrate faultyindications of remaining battery voltage. The disclosed method alsoimproves patient care as a result of less interruptions during aninfusion therapy.

FIG. 1 is a flow diagram illustrating an example artificial neuralnetwork, in accordance with an exemplary embodiment of the presentdisclosure. The artificial neural network, which is utilized in thepresent disclosure, includes an input layer, one or more hidden layers,and an output layer. The input layer may include nodes (e.g., X₁, X₂, .. . X_(N)) corresponding to a plurality of battery parameters. In oneembodiment, six battery parameters may be used, which may result in aneural network structure having six input nodes. Each hidden layer mayinclude a plurality of nodes for optimization (e.g., Z₁, Z₂ . . .Z_(M)). The optimization may occur by way of forward propagation, backpropagation, and a calibration of weights and biases. In one embodiment,the neural network may include a single hidden layer comprising eightnodes for optimization. The output layer may generate one or more outputparameters (e.g., Y₁, Y₂, . . . Y_(K)). In one embodiment, the neuralnetwork model may include three output nodes to correspond to threerespective output parameters, representing indications of “low,” “verylow,” and “depleted” remaining battery capacities. Since the input datafrom a battery may be continuous and accumulated, the three sets ofoutput data can be consolidated into one neural network model.

While input data may be supplied at the input layer, each node mayreceive a combination of one or more input variables, e.g., from nodesof the preceding layer. For example, the combination of inputs, a_(j),for the j^(th) node in the hidden layer can be expressed as thefollowing equation:

${a_{j} = {{\sum\limits_{i = 1}^{N}{\omega_{ji}^{(1)}x_{i}}} + b_{j}^{(1)}}},$

where j=1, M, the superscript (1) indicates that the correspondingparameter is in the first layer of the network, the parameters ω_(jl)⁽¹⁾ are weights, and the parameters b_(j) ⁽¹⁾ are biases. A nonlinearactivation function h(.) can provide an output (Z_(j)) of each node ofthe hidden layer, which can be expressed as:

Z _(i) =h(a ₁)

A sigmoid function can be used as the activation function, which can beexpressed as:

${h(a)} = \frac{1}{1 + {\exp\left( {- a} \right)}}$

Furthermore, the layers may be combined to find the overall neuralnetwork function:

${{y_{k}\left( {x,\omega,b} \right)} = {h\left( {{\sum\limits_{j = 1}^{M}{\omega_{kj}^{(2)}{h\left( {{\sum\limits_{i = 1}^{N}{\omega_{ji}^{(1)}x_{i}}} + b_{j}^{(1)}} \right)}}} + b_{k}^{(2)}} \right)}},$

Hence, the neural network model may comprise a nonlinear function from aset of input variables {x_(i)} to a set of output variables {y_(k)}. Inone embodiment, there are seven input variables and three outputvariables. There may be as few as two input variables and as many astwelve input variables.

Given a training set comprising a set of input vectors {x_(i)}, wheren=1, N, together with a corresponding set of target vectors {t_(n)},training a neural network may involve minimizing the error function,also called a loss function, by a mean square error (“MSE”) method. Forthe first iteration of computations through the nodes of each of thelayers of the neural network (e.g., from the input layer to the outputlayer), initial weight factors and biases are randomly selected and/orinitialized. Then, through feed-forward calculations, the loss can becalculated using the following formula:

${E(\omega)} = {{1/2}{\sum\limits_{n = 1}^{K}{{{y\left( {x_{n},\omega} \right)} - t_{n}}}^{2}}}$

If the loss is larger than a predefined tolerance, the weight factorscan be revised before the next iteration starts. The neural networkmodel may be considered trained when the predefined tolerance has beenachieved (e.g., the loss is lower than the predefined tolerance).

To improve computational efficiency, an error backpropagation method canbe utilized. For example, optimization of the parameter, ω_(jl) ⁽¹⁾ inFIG. 1 can be achieved via the expression:

$\frac{\partial E}{\partial\omega_{MN}^{(1)}} = {\frac{\partial E}{\partial y_{K}} = {\frac{\partial y_{K}}{\partial Z_{M}}*\frac{\partial Z_{M}}{\partial\omega_{MN}^{(1)}}}}$

Furthermore, the revised ω_(jl) ⁽¹⁾ can be achieved by using stochasticgradient descent optimization, using the following expression:

$\omega_{ji}^{{(1)} +} = {\omega_{ji}^{(1)} + {\eta\frac{\partial E}{\partial\omega_{ji}^{(1)}}}}$

where η is the learning step size, and ω_(jl) ⁽¹⁾⁺ is the updated weightfactor. The above described method for optimizing a given parameter,ω_(jl) ⁽¹⁾ can be applied for all parameters in order to perform thenext iteration.

In some embodiments, the number of back-propagation iterations for aneural network model to detect and generate alarms for remaining batteryvoltage is approximately 5000 iterations. In some embodiments, thenumber of iterations is limited to 5000 to prevent a situation of“over-training” the model where the model becomes overly tuned to thespecific training data set.

In one embodiment, the following features may be used to obtain andbuild input data for the neural network model: a time stamp (e.g., forfurther calculations); a measurement of a voltage (e.g., to be useddirectly as an input value for the neural network model); a measurementof current (e.g., for further filtering of the input data); ameasurement of temperature (e.g., to be used directly as an input valuefor the neural network model); a measurement of an average current(e.g., to be used directly as an input value for the neural networkmodel); a remaining voltage (e.g., to be used directly as an input valuefor the neural network model); and a full charge voltage of the battery(e.g., to be used directly as an input value for the neural networkmodel, for example, to indicate a battery state of health, and/or toprovide an indication of battery age).

The sample time stamp may be used along with the voltage to create ameasurement of a change in voltage over an interval of time (e.g.,“delta milli-volts per second”), which can be used directly as an inputvalue for the neural network model. The change in voltage may be used toprovide the neural network model with a sense of rate of change overtime.

The “current” feature may be used to determine when a rechargeablebattery switched from a charging mode to a discharging mode.Understanding this switch may be important to build the neural networkmodel, as it primarily involves the discharging portion of the cycle.

After filtering out the charging mode samples from the data set, the“remaining voltage” feature can be used to find the point where thebattery is depleted (e.g., where the “remaining voltage or capacity” iszero or near zero). From this point, additional training fields can beadded to denote when the low battery, very low battery, and depletedbattery alarms should occur. Since these samples were taken on a2-minute cycle, some adjustments can be made to further improve thetiming of the alarms.

In one embodiment, the alarm indicating a “depleted battery” may beissued 4 minutes from the zero “remaining voltage point. The alarmindicating “very low battery” may start 14 minutes from the alarm for“depleted battery.” The alarm indicating “low battery” may start 30minutes before the alarm for “depleted battery.”

Given the above, the final preprocessed training data may include, butis not limited to, the following fields: a voltage; a change in voltageover an interval of time (e.g., delta mili-volts per second); an averagecurrent; a temperature; a remaining voltage; a full charge voltage(e.g., a battery state of health); a “low battery” alarm indication; a“very low battery” alarm indication; and a “depleted battery” alarmindication. The first six fields represent features for correspondinginput values in FIG. 2, and the final three fields represent featuresfor the output values shown in FIG. 3, as described further below.

FIG. 2 is a diagram illustrating an input data matrix for using anartificial neural network for detecting and generating alarms based onremaining battery voltage, according to an embodiment of the presentdisclosure. As shown in FIG. 2, the six columns represent six featuresfor input data that can be captured periodically from an infusion pump,e.g., in real-time. As shown in FIG. 2, X[i,j] represents input vectorsincluding voltage, a change or difference in voltage over intervals oftime (e.g., every second), an average current, a remaining batteryvoltage (e.g., as calculated by a battery gas gauge IC), a batterytemperature, and a full charge voltage (battery state of health). Theinput data for these features may be sampled (e.g., received viasensors) at predetermined time intervals (e.g., every two minutes).

FIG. 3 is a diagram showing the corresponding target vectors for theinput data matrix of FIG. 2, according to an embodiment of the presentdisclosure. As shown in FIG. 3, Y[i,j] represents all target vectorsincluding low, very low, and depleted battery status. In one embodiment,Y[i,j] may equal zero when the respective status is false, but Y[i,j]may equal one when the respective status is true.

FIG. 4 is a flow chart illustrating an example method for training aneural network for detecting and generating alarms for remaining batteryvoltage, according an embodiment of the present disclosure. In anexample method of training the neural network, a reference featurevector may be received for each of a plurality of reference trainingdata. The reference training data may be obtained during discharging ofone or more reference batteries. Additionally or alternatively, thereference training data set may be a subset (e.g., half) of the testdata set. Each reference feature vector may comprise a predeterminednumber of inputs, for example, a reference voltage of a referencebattery; a change in the reference voltage over a predetermined intervalof time; a reference average current associated with the referencebattery; a reference temperature associated with the reference battery;and a reference remaining voltage reported by a battery gas gaugeintegrated circuit (“IC”) associated with the reference battery. Inother embodiments, fewer inputs or additional inputs may be used.

In the example shown in FIG. 4, each received reference vector maycomprise six inputs. Furthermore, the training method may includeinputting the corresponding target value vectors (“output vectors”) forthe reference feature vectors. As previously discussed, each targetvalue vector may provide an indicia of the remaining battery voltage forthe corresponding input data. For example, the target value may indicatewhether the remaining battery voltage meets the thresholds for a “lowbattery,” “very low battery,” or a “depleted battery” indication. Thereference feature vectors may be associated with their correspondingoutput vectors. The neural network model may be trained using theassociated reference feature vectors to output weight factors and biasesfor each path of the neural network model. The training may include aniterative process comprising a feed forward propagation through thelayers of the neural network model, a calculation of a loss function,and a backpropagation through the layers of the neural network model.However, at the first iteration, the training method may initialize byrandomly generating weights and biases. After errors are minimized(e.g., the loss is within a tolerance level), the training method mayreturn weight factors and biases for each path. These weight factors andbiases associated with the trained neural network may be stored, e.g.,for use in applying the trained neural network as shown in FIG. 5.

FIG. 5 is a flow chart illustrating an example method for applying atrained neural network for detecting and generating alarms for remainingbattery voltage, according an embodiment of the present disclosure.Specifically, FIG. 5 illustrates a feed-forward computation usingreal-time battery data. For example, an infusion device being powered bya rechargeable battery may receive, at predetermined intervals of timein real-time, measurements comprising: a voltage of the rechargeablebattery, a change in the voltage over the predetermined interval oftime, an average current associated with the rechargeable battery, atemperature of the rechargeable battery, and a remaining voltage orcapacity reported by a battery gas gauge integrated circuit (“IC”). Thesix types of input data may be used to form input feature vectors withpreconditioning. The infusion device may also identify and retrieve thestored weight factors and biases from the training method of FIG. 4(e.g., parameters ω_(ij) and b_(j)). The six-feature input vectors andstored weight factors and biases may be inputted into a neural networkto generate an indicia of actual remaining voltage or capacity of therechargeable battery (e.g., whether the rechargeable battery has “lowbattery,” “very low battery,” or “depleted battery” status). Theapplication of the neural network model may involve performingfeed-forward computations, computing inputs at each node of the hiddenlayer, and computing activation functions at each node.

In some embodiments, an infusion device selects a trained neural networkand corresponding weight factors/biases parameters ω_(jl) and b_(j)based on a known type of battery. In these instances, the battery gasgauge IC may transmit an identifier of a type of battery, which mayspecify a model number, manufacturer, version, etc. The infusion deviceuses the battery information from the battery gas gauge IC to select thecorresponding rained neural network and corresponding weightfactors/biases parameters ω_(ji) and b₁, which may be stored locally orremotely at a server.

As previously discussed, systems and methods of the present disclosurehelp to overcome the inaccuracies of using the battery's internal gasgauge IC to measure remaining battery voltage or capacity and generatealerts. Conventionally, these inaccuracies made it necessary to add amargin to the calculated run-time remaining value so that the batterycould be guaranteed to have enough energy to allow an infusion tocontinue for the required amount of time after a low or very low batteryalarm was issued. A desired outcome of using the neural network modeldiscussed in the present disclosure is to reduce or eliminate the needfor this margin. The disclosed methods for detecting and generatingalarms for remaining battery voltage or capacity enables the infusionsystem to run for a longer period of time on battery power. To examinethis possibility, the existing cache of battery alarm time data wasanalyzed, as shown in the table below.

Ideal Method Conventional Method Remaining battery Time until 30 15 3015 voltage/capacity battery empty minute minute Depleted minute minuteDepleted (Wh) (minutes) alarm alarm alarm alarm alarm alarm 9.9 86 0 0 00 0 0 9.7 84 0 0 0 0 0 0 9.5 82 0 0 0 0 0 0 9.3 80 0 0 0 0 0 0 9.1 78 00 0 1 0 0 8.8 76 0 0 0 1 0 0 8.6 74 0 0 0 1 0 0 8.4 72 0 0 0 1 0 0 8.270 0 0 0 1 0 0 7.9 68 0 0 0 1 0 0 7.7 66 0 0 0 1 0 0 7.5 64 0 0 0 1 0 07.3 62 0 0 0 1 0 0 7.0 60 0 0 0 1 1 0 6.8 58 0 0 0 1 1 0 6.6 56 0 0 0 11 0 6.3 54 0 0 0 1 1 0 6.1 52 0 0 0 1 1 0 5.9 50 0 0 0 1 1 0 5.6 48 0 00 1 1 0 5.4 46 0 0 0 1 1 0 5.2 44 0 0 0 1 1 1 4.9 42 0 0 0 1 1 1 4.7 400 0 0 1 1 1 4.5 38 0 0 0 1 1 1 4.2 36 0 0 0 1 1 1 4.0 34 1 0 0 1 1 1 3.832 1 0 0 1 1 1 3.5 30 1 0 0 1 1 1 3.3 28 1 0 0 1 1 1 3.1 26 1 0 0 1 1 12.8 24 1 0 0 1 1 1 2.6 22 1 0 0 1 1 1 2.4 20 1 0 0 1 1 1 2.1 18 1 1 0 11 1 1.9 16 1 1 0 1 1 1 1.6 14 1 1 0 1 1 1 1.4 12 1 1 0 1 1 1 1.2 10 1 10 1 1 1 0.9 8 1 1 0 1 1 1 0.6 6 1 1 0 1 1 1 0.4 4 1 1 1 1 1 1 0.1 2 1 11 1 1 1 0.0 0 1 1 1 1 1 1

As shown in the above table, computations were added to the set ofbattery data to duplicate the existing run-time remaining algorithm thatis implemented in the disclosed infusion system. Using this computedrun-time remaining value, the times at which the current algorithm wouldissue the low, very low, and depleted battery alarms were added to thedata. This was compared to the ideal time at which those alarms shouldbe issued. The cells in the table having a value of ‘1’ show when theconventional method for generating an alarm, and the disclosed methodfor generating an alarm would issue the low (30 minute), very low (15minute), and depleted battery (3 minutes) alarms.

The above table shows that the previous known algorithm used in theinfusion system has a significant difference between when it issues thebattery alarms versus the ideal time at which those alarms should beissued. For example, the previous known algorithm issues the low batteryalarm when the battery is 78 minutes from empty. In the ideal case, thelow battery alarm would be issued when the battery's time until empty isequal to 33 minutes (30 minutes of infusion run-time, plus an additional3 minutes when the infusion system is alarming before it shuts downcompletely). This is 45 minutes of additional run-time on battery thatis lost due to the margin that is needed for the previous knownalgorithm.

As mentioned previously, the times at which the disclosed methodutilizing the disclosed neural network model issued the battery alarmscorrelated very closely to the times issued by an ideal algorithm. Thus,the disclosed approach can be considered to match the ideal case most ofthe time. Using the neural network can allow a reduction in the run-timeremaining margin, and therefore a longer run-time on battery.

Example Infusion System and Infusion Device

FIG. 6 is a diagram of an infusion device 600 that is configured to usea trained neural network 602 to perform the method of FIG. 5, accordingto an example embodiment of the present disclosure. The infusion device600 is an infusion pump, such as a syringe pump, an ambulatory pump, ora peristaltic pump. The infusion device 600 is connected to a rack 604for support.

The infusion device 600 is configured to receive IV tubing 606. In anexample, a cover 608 of the infusion device 600 opens, enabling the IVtubing 606 to be inserted. A first end of the IV tubing 606 a is fluidlycoupled to a fluid container that holds a drug, medication, or otherfluid for an infusion treatment. A second end of the IV tubing 606 b isfluidly coupled to a patient via an intravenous connection.

The infusion device 600 includes a user interface 610 for receivingoperator inputs (e.g., a flow rate) such as the one or more parametersdiscussed above. The user interface 610 also displays informationincluding a status of an infusion treatment and alarms/alerts indicativeof a low battery including the “low” battery alert, the “very low”battery alert, and the “depleted” battery alert. The user interface 610includes a touchscreen and a keypad. In other embodiments, the userinterface 610 may include only a touchscreen or a keypad.

The infusion device 600 of FIG. 6 also includes a processor 612, amemory 614, and a communication module 616. While one processor 612 isshown, the infusion device 600 may include a plurality of processors.The processor 612 includes a controller, a logic device, etc. configuredto execute the trained neural network 602 (e.g., an algorithm) stored inthe memory 614. The processor 612 is also configured to execute one ormore instructions stored in the memory 614 that, when executed by theprocessor 612, cause the processor 612 to perform the operationsdescribed herein to provide an infusion treatment. The memory 614includes any memory device including read only memory, flash memory,random access memory, a hard disk drive, a solid state drive, etc.

The communication module 616 is configured for wireless and/or wiredcommunication with a network, such as the Internet, a cellular network,and/or a local hospital network. The communication module 616 may beconfigured, for example, for Wi-Fi or Ethernet communication. In theillustrated example, the communication module 616 is configured toreceive the trained neural network 602 (including weight factors/biasesparameters ω_(jl) and b_(j)) from a server or clinician computer via anetwork. In other examples, the processor 612 may perform the method ofFIG. 5 to train the neural network 602. The communication module 616 mayalso receive one or more parameters specifying an infusion treatment tobe performed. Further, the communication module 616 may transmit alertor alarm messages to a server when a low battery is detected.

The infusion device 600 of FIG. 6 further includes a drive mechanism618, a motor 620, a battery/power regulator 622, and a battery gas gaugeIC 624. Together, the drive mechanism 618 and the motor 620 comprise apumping mechanism. The processor 612 is configured to transmit signalsor commands to the motor 620, which cause the motor 620 to rotate orotherwise operate in a certain direction and speed. The movement orrotation of a drive shaft of the motor 620 causes the drive mechanism618 to actuate or otherwise provide force on the IV tubing 606 (or afluid container in alternative embodiments where a fluid container isplaced inside the infusion device). The drive mechanism 618 may includefinger actuators or a rotary actuator that apply pressure on the IVtubing 606 to deliver fluid from the fluid container to a patient for aninfusion treatment. The drive mechanism 618 and the motor 620 arecollectively configured to provide precise control of fluid deliverybetween 0.1 milliliters/hour up to 1000 milliliters/hour.

For a syringe pump, the drive mechanism 618 may include a piston orother actuator that pushes on a plunger of a syringe. In someembodiments, the motor 620 may rotate a drive screw, which causes thedrive mechanism 618 to apply force on the plunger.

The battery/power regulator 622 is configured to provide electricalpower for the infusion device 600. A power regulator converts outletbased AC power into DC power. A battery provides constant DC power. Thebattery is rechargeable battery via the AC power. The battery gas gaugeIC 624 transmits information regarding the battery 622 to the processor612. The information may include a type of the battery 622, which isused for selecting the trained neural network 602 from the memory 614.The information also includes remaining voltage/capacity of the battery622. The information may further include measurements including avoltage of the rechargeable battery, a change in the voltage over thepredetermined interval of time, an average current associated with therechargeable battery, and/or a temperature of the rechargeable battery.

Alternatively, the processor 612 determines or receives in real-time atpredetermined intervals of time, measurements including a voltage of therechargeable battery 622, a change in the voltage over the predeterminedinterval of time, an average current associated with the rechargeablebattery, and/or a temperature of the rechargeable battery. As discussedabove in connection with FIG. 5, the processor 612 applies the receivedinformation as inputs to the trained neural network 602 and receives anoutput indicative of a status of the battery 622. Generally, the statusindicates that the battery 622 has sufficient charge. However, thetrained neural network 602 outputs the low battery, very low battery, ordepleted battery status when the inputs are indicative of that batterystate. The processor 612 is configured to display an alarm/alert toindicate when the low battery, very low battery, or depleted batterystatus is present. Further, the processor 612 uses the communicationmodule 616 to transmit the battery alarm/alert to a network.

The processor 612 may include one or more sensors 626 for measuring oneor more of a voltage of the rechargeable battery 622, a change in thevoltage over the predetermined interval of time, an average currentassociated with the rechargeable battery, and/or a temperature of therechargeable battery. In other instances, the sensors 626 are providedin proximity to the battery 622 and communicatively coupled to theprocessor 612. The sensors 626 may include a voltage meter, a currentmeter, and/or a temperature gauge. In some instances the voltage meterand the current meter may be integrated with the processor 612 while thetemperature gauge is provided in proximity to the battery 622.

In some embodiments, the processor 612 compares the battery statusoutput from the trained neural network 602 to a time remaining for aninfusion treatment. If the battery status indicates that the batterywill be depleted before the infusion treatment is timed to end, theprocessor 612 may generate a more pronounced alarm on the user interface610 and/or for transmission to the network to indicate an infusiontreatment will not be adequately completed.

It should be appreciated that the processor 612 performs a battery statedetermination during infusion treatments and when infusion treatmentsare not in progress. As such, the trained neural network 602 uses thechange in battery voltage over time to assess how quickly the battery622 is being drained, which corresponds to whether an infusion treatmentis being performed. The processor 612 performs the battery statedetermination at periodic intervals, such as every 50 milliseconds, 100milliseconds, 500 milliseconds, 1 second, 2 seconds, 5 seconds, 30seconds, 1 minute, etc.

It should also be appreciated that the depleted battery statecorresponds to a battery voltage where the gas gauge IC 624 prevents anyfurther drain from the battery 622. To prevent permanent damage to thebattery from a complete drain, the gas gauge IC 624 may prevent furthercurrent drain when the battery 622 has at least some charge, such as 0.5volts or 0.1 volts. In some embodiments, the processor 612 may cause theinfusion device 600 to enter a fail-safe mode after the depleted batterystate is reached. The fail-safe mode may include a controlled poweringdown of the infusion device 600.

FIG. 7 is a diagram of an infusion system 700 configured to perform theoperations described in connection with FIG. 4, according to an exampleembodiment of the present disclosure. The infusion system 700 includesthe infusion device 600 of FIG. 6. The infusion system 700 also includesa server 702 that is connected to the infusion device 600 via a network704, which may include any cellular, wide area, and/or local areanetwork. The server 702 may be part of a heath information system andinclude a clinician computer.

In the illustrated example, the server 702 receives reference trainingdata 706, such as the reference feature vectors discussed above. Thetraining data 706 may be input into the server 702 from manuallyobtained data. Additionally or alternatively, the training data 706 maybe received from one or more infusion devices including the infusiondevice 600.

As discussed above, the server 702 is configured to create one or moretrained neural networks 602 for types of batteries using the trainingdata 706. The server 702 may transmit the trained neural networks 602 tothe infusion device 600 via the network 704. Alternatively, the server702 may receive battery type information from the infusion device 600(via the gas gauge IC 624) before a treatment is to be begin. The server702 selects the trained neural network 602 that matches or correspondsto the received battery information and transmits the selected trainedneural network 602 (and weight factors/biases parameters ω_(ji) andb_(j)) to the infusion device 600 for battery state detection.

CONCLUSION

It should be understood that various changes and modifications to thepresently preferred embodiments described herein will be apparent tothose skilled in the art. Such changes and modifications can be madewithout departing from the spirit and scope of the present subjectmatter and without diminishing its intended advantages. It is thereforeintended that such changes and modifications be covered by the appendedclaims.

The invention is claimed as follows:
 1. An infusion device comprising: arechargeable battery having a gas gauge integrated circuit (“IC”); oneor more processors; and memory storing instructions that, when executedby the one or more processors, cause the one or more processors to: atleast one of receive or determine, at predetermined intervals of time inreal-time, measurements comprising: a voltage of the rechargeablebattery, a change in the voltage over the predetermined interval oftime, an average current associated with the rechargeable battery, atemperature of the rechargeable battery, and a remaining voltage orcapacity reported by the gas gauge IC, generate a feature vectorcomprising the voltage, the change in the voltage, the average current,the temperature, the remaining voltage or capacity reported by the gasgauge IC, and a full charge voltage or capacity of the rechargeablebattery, apply the feature vector to a trained neural network todetermine an actual remaining voltage or capacity of the rechargeablebattery, wherein the trained neural network comprises weight factors andbiases for calculating a plurality of paths through a plurality oflayers, and generate, in real-time, an alarm indicating that the actualremaining voltage or capacity of the rechargeable battery is below apredetermined threshold when the actual remaining voltage or capacity ofthe rechargeable battery is below the predetermined threshold.
 2. Theinfusion device of claim 1, wherein the predetermined threshold includesa first threshold corresponding to a low battery state, a secondthreshold corresponding to a very low battery state, and a thirdthreshold corresponding to a depleted battery state.
 3. The infusiondevice of claim 2, wherein the trained neural network is configured to:use the feature vector to determine if any of the first, second, orthird thresholds are satisfied; when the first threshold is reached andthe second threshold is not reached, indicate the low battery state forthe alarm; when the first and second thresholds are reached and thethird threshold is not reached, indicate the very low battery state forthe alarm; and when the first, second, and third thresholds are reached,indicate the depleted battery state for the alarm.
 4. The infusiondevice of claim 2, wherein the low battery state corresponds to 30minutes before the depleted battery state is reached and the very lowbattery state corresponds to 15 minutes before the depleted batterystate is reached.
 5. The infusion device of claim 4, wherein thedepleted battery state corresponds to three to four minutes before therechargeable battery is depleted and can no longer provide power.
 6. Theinfusion device of claim 1, wherein the one or more processors areconfigured to generate feature vectors and apply the feature vectors inreal-time to the trained neural network at periodic intervals includingat least one of every 50 milliseconds, 100 milliseconds, 500milliseconds, 1 second, 2 seconds, 5 seconds, 30 seconds, or 1 minute.7. The infusion device of claim 1, wherein the one or more processorsare configured to transmit the alarm to a server via a network.
 8. Theinfusion device of claim 1, wherein the one or more processors areconfigured to display an indication of the alarm on a user interface. 9.An infusion device comprising: a rechargeable battery having a gas gaugeintegrated circuit (“IC”); a user interface; a battery sensor; one ormore processors; and memory storing a plurality of trained neuralnetworks for different rechargeable battery types and instructions that,when executed by the one or more processors, cause the one or moreprocessors to: receive from the gas gauge IC information indicative of atype of the rechargeable, select one of the trained neural networksbased on the information from the gas gauge IC, at least one of receiveor determine, at predetermined intervals of time in real-time,measurements comprising: a voltage of the rechargeable battery from thebattery sensor, a change in the voltage over the predetermined intervalof time, an average current associated with the rechargeable batteryfrom the battery sensor, a temperature of the rechargeable battery fromthe battery sensor, and a remaining voltage or capacity reported by thegas gauge IC, generate a feature vector comprising the voltage, thechange in the voltage, the average current, the temperature, theremaining voltage or capacity reported by the gas gauge IC, and a fullcharge voltage or capacity of the rechargeable battery, apply thefeature vector to the selected trained neural network to determine anactual remaining voltage or capacity of the rechargeable battery,wherein the trained neural network comprises weight factors and biasesfor calculating a plurality of paths through a plurality of layers, andgenerate, in real-time, an alarm indicating that the actual remainingvoltage or capacity of the rechargeable battery is below a predeterminedthreshold when the actual remaining voltage or capacity of therechargeable battery is below the predetermined threshold.
 10. Theinfusion device of claim 9, wherein the predetermined threshold includesa first threshold corresponding to a low battery state, a secondthreshold corresponding to a very low battery state, and a thirdthreshold corresponding to a depleted battery state.
 11. The infusiondevice of claim 10, wherein the trained neural network is configured to:use the feature vector to determine if any of the first, second, orthird thresholds are satisfied; when the first threshold is reached andthe second threshold is not reached, indicate the low battery state forthe alarm; when the first and second thresholds are reached and thethird threshold is not reached, indicate the very low battery state forthe alarm; and when the first, second, and third thresholds are reached,indicate the depleted battery state for the alarm.
 12. The infusiondevice of claim 9, wherein the one or more processors are configured totransmit the alarm to a server via a network.
 13. The infusion device ofclaim 9, wherein the one or more processors are configured to display anindication of the alarm on the user interface.
 14. An infusion systemcomprising: a server configured to generate a plurality of trainedneural networks; and an infusion device communicatively coupled to theserver via a network, the infusion device including: a rechargeablebattery having a gas gauge integrated circuit (“IC”); one or moreprocessors; and memory storing instructions that, when executed by theone or more processors, cause the one or more processors to: receive atleast one trained neural network from the server, wherein the receivedtrained neural network comprises weight factors and biases forcalculating a plurality of paths through a plurality of layers, receive,at predetermined intervals of time in real-time, measurementscomprising: a voltage of the rechargeable battery, a change in thevoltage over the predetermined interval of time, an average currentassociated with the rechargeable battery, a temperature of therechargeable battery, and a remaining voltage or capacity reported bythe gas gauge IC, generate a feature vector comprising the voltage, thechange in the voltage, the average current, the temperature, theremaining voltage or capacity reported by the gas gauge IC, and a fullcharge voltage or capacity of the rechargeable battery, apply thefeature vector to the received trained neural network to determine anactual remaining voltage or capacity of the rechargeable battery, andgenerate, in real-time, an alarm indicating that the actual remainingvoltage or capacity of the rechargeable battery is below a predeterminedthreshold when the actual remaining voltage or capacity of therechargeable battery is below the predetermined threshold.
 15. Theinfusion system of claim 14, wherein the server is configured to:generate the plurality of trained neural networks for differentrechargeable battery types; receive an indication of a rechargeablebattery type of the infusion device; select a trained neural networkthat corresponds to the rechargeable battery type at the infusiondevice; and transmit the selected trained neural network to the infusiondevice.
 16. The infusion system of claim 15, wherein the server isconfigured to, for each rechargeable battery type: generate, for each ofa plurality of reference data obtained during discharging of referencebatteries, a reference feature vector comprising: a reference voltage ofa reference battery, a change in the reference voltage over apredetermined interval of time, a reference average current associatedwith the reference battery, a reference temperature associated with thereference battery, and a reference remaining voltage or capacityreported by a battery gas gauge integrated circuit (“IC”) associatedwith the reference battery; associate, for each of the plurality ofreference data, the reference feature vector with a corresponding outputvector indicating an actual reference remaining voltage or capacity; andtrain, using the associated reference feature vectors, one of the neuralnetworks to determine the actual remaining voltage or capacity of therechargeable battery type.
 17. The infusion system of claim 14, whereinthe predetermined threshold includes a first threshold corresponding toa low battery state, a second threshold corresponding to a very lowbattery state, and a third threshold corresponding to a depleted batterystate.
 18. The infusion system of claim 17, wherein the received trainedneural network is configured to: use the feature vector to determine ifany of the first, second, or third thresholds are satisfied; when thefirst threshold is reached and the second threshold is not reached,indicate the low battery state for the alarm; when the first and secondthresholds are reached and the third threshold is not reached, indicatethe very low battery state for the alarm; and when the first, second,and third thresholds are reached, indicate the depleted battery statefor the alarm.
 19. The infusion system of claim 14, wherein the one ormore processors are configured to transmit the alarm to a server via thenetwork.
 20. The infusion system of claim 14, wherein the one or moreprocessors are configured to display an indication of the alarm on theuser interface.